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Nonparametric estimation using uniform-width binning is a standard approach for evaluating the calibration performance of machine learning models.
The well-calibrated Bayesian
A. P. Dawid · 1982
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Backpropagation applied to handwritten zip code recognition
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel · 1989
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Probabilistic outputs for support vector machines and comparison to regularized likelihood methods
J. Platt · 1999
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Random forests
L. Breiman · 2001
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Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
B. Zadrozny and C. Elkan · 2001
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Transforming classifier scores into accurate multiclass probability estimates
B. Zadrozny and C. Elkan · 2002
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PAC-Bayesian stochastic model selection
D. A. McAllester · 2003
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A note on the PAC Bayesian theorem
A. Maurer · 2004
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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
C. E. Rasmussen and C. K. I. Williams · 2005
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Introduction to Nonparametric Estimation
A. B. Tsybakov · 2008
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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Are we ready for autonomous driving? The KITTI vision benchmark suite
A. Geiger · 2012
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Calibrating predictive model estimates to support personalized medicine
X. Jiang, M. Osl, J. Kim, and L. Ohno-Machado · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E Hinton · 2012
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PAC-Bayesian generalization bound on confusion matrix for multi-class classification
E. Morvant, S. Koço, and L. Ralaivola · 2012
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Concentration Inequalities: A Nonasymptotic Theory of Independence
S. Boucheron, G. Lugosi, and P. Massart · 2013
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PAC-Bayesian AUC classification and scoring
J. Ridgway, P. Alquier, N. Chopin, and F. Liang · 2014
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Deepdriving: Learning affordance for direct perception in autonomous driving
C. Chen, A. Seff, A. Kornhauser, and J. Xiao · 2015
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Obtaining well calibrated probabilities using Bayesian binning
M. P. Naeini, G. F. Cooper, and M. Hauskrecht · 2015
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On the properties of variational approximations of Gibbs posteriors
P. Alquier, J. Ridgway, and N. Chopin · 2016
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XGBoost: A scalable tree boosting system
T. Chen and C. Guestrin · 2016
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Distribution-free binary classification: prediction sets, confidence intervals and calibration
C. Gupta, A. Podkopaev, and A. Ramdas · 2020
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Fantastic generalization measures and where to find them
Y. Jiang, B. Neyshabur, H. Mobahi, D. Krishnan, and S. Bengio · 2020
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Non-parametric calibration for classification
J. Wenger, H. Kjellström, and R. Triebel · 2020
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Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning
J. Zhang, B. Kailkhura, and T Y.-J. Han · 2020
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How tight can PAC-Bayes be in the small data regime?
A. Foong, W. Bruinsma, D. Burt, and R. Turner · 2021
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Distribution-free calibration guarantees for histogram binning without sample splitting
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Wide residual networks
S. Zagoruyko and N. Komodakis · 2016
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On calibration of modern neural networks
C. Guo, G. Pleiss, Y. Sun, and K. Q Weinberger · 2017
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Densely connected convolutional networks
G. Huang, Z. Liu, L. van der Maaten, and K. Q Weinberger · 2017
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Beta calibration: A well-founded and easily implemented improvement on logistic calibration for binary classifiers
M. Kull, T. S. Filho, and P. Flach · 2017
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Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2017
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Information-theoretic analysis of generalization capability of learning algorithms
A. Xu and M. Raginsky · 2017
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C. Gupta and A. Ramdas · 2021
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Calibration tests beyond classification
D. Widmann, F. Lindsten, and D. Zachariah · 2021
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Better uncertainty calibration via proper scores for classification and beyond
S. Gruber and F. Buettner · 2022
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A consistent and differentiable lp canonical calibration error estimator
Teodora Popordanoska, Raphael Sayer, and Matthew Blaschko · 2022
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Mitigating bias in calibration error estimation
R. Roelofs, N. Cain, J. Shlens, and M. C Mozer · 2022
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How does information bottleneck help deep learning?
Kenji Kawaguchi, Zhun Deng, Xu Ji, and Jiaoyang Huang · 2023
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Minimum-risk recalibration of classifiers
Z. Sun, D. Song, and A. Hero · 2023
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Information-theoretic generalization analysis for expected calibration error
Futoshi Futami and Masahiro Fujisawa · 2024
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PAC-Bayes generalization certificates for learned inductive conformal prediction
A. Sharma, S. Veer, A. Hancock, H. Yang, M. Pavone, and A. Majumdar · 2024
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